Homeostatic regulation across fast and slow timescales through aggregate synaptic dynamics
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Learned information and experiences are thought to be stored in synapses, composed of building block molecules whose number typically correlates with synaptic strength. Activity-dependent plasticity mechanisms, such as Hebbian learning, regulate these building blocks, promoting synaptic growth to encode acquired knowledge. However, this process can destabilize cortical networks through overexcitation, leading to runaway dynamics. To prevent such instabilities the brain uses compensatory mechanisms like synaptic scaling. Existing models rely on rapid timescales, contradicting experimental observations that synaptic scaling occurs slowly. Here, we introduce aggregate scaling, a simple framework to study synapse-mediated homeostasis based on the availability and competitive redistribution of synaptic building blocks. Our model enforces stability by integrating rapid regulation of the total synaptic strength and firing rate homeostasis over much slower, realistic timescales. It preserves key neuronal properties, such as firing activity around a homeostatic set-point, long-tailed distributions of synaptic weights, and response to brief stimulation.
Author summary
Learning and memory rely on changes in the strength of synapses, connections between neurons. When these changes go unchecked, they can lead to abnormal brain activity. Homeostatic mechanisms such as synaptic scaling seem to contribute to the brain’s solution to this problem. However, most existing models of synaptic scaling require this process to be much faster than what is observed in real neurons, raising doubts about their biological accuracy. In our study, we present aggregate scaling, an alternative framework where synapses share and compete for limited molecular resources needed to maintain their strength. This competition is paired with a homeostatic regulation of the abundance of synaptic rescources. We show that this allows neurons and networks to remain stable while still supporting learning. Unlike previous models, our approach operates on biologically realistic timescales and reproduces key experimental findings, including stable neural activity and natural patterns of synaptic strength. Overall our model provides insights into how the brain maintains balance during learning, emphasizing the importance of synaptic resource logistics.
